Create app.py

app.py

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+import gradio as gr
+import spaces
+from diffusers import FluxPipeline
+import torch
+
+# Replace with your actual model and LoRA paths
+BASE_MODEL = "black-forest-labs/FLUX-1-dev"
+LORA_MODEL = "your_username/your_flux_lora" # Replace with your repo
+
+# Load base model pipeline
+pipe = FluxPipeline.from_pretrained(BASE_MODEL, torch_dtype=torch.float16)
+
+# Load LoRA weights
+pipe.unet.load_attn_procs(LORA_MODEL)
+pipe = pipe.to("cuda")
+
+@spaces.GPU
+def generate_image(prompt, num_inference_steps=25, guidance_scale=7.5, seed=None):
+    """Generates an image using the FLUX.1-dev LoRA model."""
+    generator = torch.Generator("cuda").manual_seed(seed) if seed else None
+
+    image = pipe(
+        prompt,
+        num_inference_steps=num_inference_steps,
+        guidance_scale=guidance_scale,
+        generator=generator,
+    ).images[0]
+    return image
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=generate_image,
+    inputs=[
+        gr.Textbox(lines=3, label="Prompt"),
+        gr.Slider(minimum=10, maximum=100, value=25, label="Inference Steps"),
+        gr.Slider(minimum=1, maximum=15, value=7.5, label="Guidance Scale"),
+        gr.Number(label="Seed (Optional)"),
+    ],
+    outputs=gr.Image(label="Generated Image"),
+    title="FLUX.1-dev LoRA Demo",
+    description="A demo of your FLUX.1-dev LoRA model.",
+)
+
+iface.launch()